Recent Articles
Recent advancements in AI technology have begun to play a crucial role in medical education. AI models, such as ChatGPT, have shown promise in various applications, including answering medical questions and assisting in clinical decision-making. However, there is limited research on the performance of these models on comprehensive medical licensing examinations.
Virtual reality (VR) technologies have demonstrated therapeutic usefulness across a variety of healthcare settings. However, graduate medical education (GME) trainee perspectives on VR acceptability and usability are limited. The behavioral intentions of GME trainees with regards to VR as an anxiolytic has not been characterized through a theoretical framework of technology adoption.
Biochemistry is a cornerstone of medical education. Its knowledge is integral to the understanding of complex biological processes and how they are applied in several areas in healthcare. Also, its significance is reflected in the way it informs the practice of medicine, which can guide and help in both diagnosis and treatment. However, the retention of biochemistry knowledge over time remains a dilemma. Long-term retention of such crucial information is extremely important, as it forms the foundation upon which clinical skills are developed and refined. The effectiveness of biochemistry education, and consequently its long-term retention, is influenced by several factors. Educational methods play a critical role; interactive and integrative teaching approaches have been suggested to enhance retention compared to traditional didactic methods. The frequency and context in which biochemistry knowledge is applied in clinical settings can significantly impact its retention. Practical application reinforces theoretical understanding, making the knowledge more accessible in the long term. Prior knowledge (familiarity) of information suggests that it is stored in long-term memory, which makes its retention in the long term easier to recall.
Chat Generative Pre-training Transformer (ChatGPT) is an artificial intelligence natural language model developed by OpenAI. It generates new texts, responds to user inputs conversationally, and can summarize and translate text. In medical application, it has been evaluated for use in areas like answering NBME Step 1 questions, with over 60% accuracy, and supporting clinical practice and scientific writing. However, its potential for improving patient outcomes and addressing healthcare disparities has not been thoroughly investigated.
The General Medicine In-training Examination (GM-ITE) tests clinical knowledge in a two-year postgraduate residency program in Japan. In the academic year 2021, as a domain of medical safety, the GM-ITE included questions regarding the diagnosis from medical history and physical findings through video viewing and the skills in presenting a case. Examinees watched a video/audio recording of a patient examination and provided free-text responses. However, the human cost of scoring free-text answers may limit the implementation of GM-ITE. A simple morphological analysis and word-matching model, thus, can be used to score free-text responses.
The digitalization of health care organizations is an integral part of a clinician’s daily life, making it vital for health care professionals (HCPs) to understand and effectively use digital tools in hospital settings. However, clinicians often express a lack of preparedness for their digital work environments. Particularly, new clinical end users, encompassing medical and nursing students, seasoned professionals transitioning to new health care environments, and experienced practitioners encountering new health care technologies, face critically intense learning periods, often with a lack of adequate time for learning digital tools, resulting in difficulties in integrating and adopting these digital tools into clinical practice.
Generative large language models (LLMs) have the potential to revolutionize medical education by generating tailored learning materials, enhancing teaching efficiency, and improving learner engagement. However, the application of LLMs in healthcare settings, particularly for augmenting small datasets in text classification tasks, remains underexplored, particularly for cost- and privacy-conscious applications that do not permit the use of third-party services such as OpenAI’s ChatGPT.
